AI STRATEGY & IMPLEMENTATION
AI strategy and implementation
Generative AI only earns budget when workloads carry logging, data boundaries, and operational ownership like any other tier-one service.
We narrow scope to use cases your organisation can sustain, then ship inference paths on managed cloud AI. Often that is AWS Bedrock or Azure AI services, with Gemini via Vertex where Google Cloud is already primary.
Anthropic Claude, OpenAI, or Meta Llama enter the picture when latency, licensing, or tenancy pushes you toward a specific API or private inference pattern.
Our service pairs strategy with engineering: readiness checks, platform wiring (identity, quotas, guardrails), integration patterns for RAG and agents, and production operations (observability, change control, and spend discipline) consistent with how you already run platforms.
Cloud & AI ecosystems
Our work centres on business-critical application landscapes on cloud. When you add AI capabilities, they follow the same thread: managed AI on hyperscalers, the platform foundations every workload shares, then model providers and APIs.
Cloud platforms
Native AI suites on the hyperscalers that already host the rest of your cloud footprint, when managed services are the right trade-off.
Microsoft Azure AI Google Cloud AI AWS BedrockPlatform foundations
The shared base for business-critical applications: clusters, Infrastructure as Code, GitOps, and observability. AI features use the same networks, identity, and controls as your other tier-one services.
Kubernetes Terraform GitOps ObservabilityModel providers & APIs
The inference layer for AI workloads: models and APIs chosen for risk, latency, tenancy, and where data may go.
Anthropic Claude OpenAI Google Gemini Meta Llama
Names identify stacks we design for and integrate with. No paid placement implied. This list highlights the AI layer; the same platform carries your wider application portfolio.
We anchor AI work in business-critical applications and platforms on public cloud. Model and vendor choices follow data classification, residency, risk tier, and running cost, not logos. We design secure ingress and egress, quotas, logging, and operational handover whether inference stays managed or sits next to workloads on Kubernetes. Labels above follow cloud surface, shared platform, then models.
Third-party names are trademarks of their respective owners. Mention here does not imply partnership, certification, or endorsement unless stated elsewhere on our site.
From strategy to production AI
Three phases that stay grounded in your data landscape, the cloud platforms you already run, and responsible AI from day one.
Strategy & discovery
We align on the right use cases, ROI, and constraints with your stakeholders, then assess AI readiness, data foundations, and a practical adoption roadmap.
Responsible AI, governance, and compliance sit inside the strategy from day one: not as a late add-on.
Implementation
We build production-grade GenAI and ML workloads with patterns your platform team can operate: secure connectivity to Anthropic, OpenAI, Gemini, or Llama surfaces; private inference where policy demands it; measurable SLOs alongside Kubernetes or managed endpoints.
Optimize & scale
We help you run AI like any other tier-one workload: telemetry across prompts and pipelines, evaluation hooks, token and infra budgets, retraining or routing changes via CI, not dashboard heroics.
What we help you deliver
End-to-end support across strategy, platforms, and safe operations.
Generative AI (LLMs, RAG, fine-tuning)
AI strategy, roadmap & use-case discovery
MLOps & AI platform engineering
AI governance, security & responsible AI
Managed AI routes: Bedrock, Azure AI / OpenAI, Vertex AI / Gemini
Claude, OpenAI, Gemini & Llama integration patterns
AI observability, evaluation hooks & spend guardrails
Model development, deployment & operations
Why work with The Factory
We combine cloud platform depth with hands-on AI delivery, so architectures, identities, and landing zones support models and data pipelines cleanly.
We pair ambition with pragmatism: measurable outcomes, clear guardrails, and realistic roadmaps, not slide decks that ignore production reality.
As an AWS Select Tier Consulting Partner and Microsoft Solutions Partner for Digital & App Innovation, we lean on managed AI services where they fit. On Google Cloud we apply the same engineering discipline to Vertex AI and Gemini routes. Everywhere, Infrastructure as Code keeps environments reproducible and auditable.
AI in production
Two recent engagements where AI moved from idea to production.
AI agents in education
We implemented an AI agent platform for educational institutions. Teachers create steerable AI tutors that give students equitable access to AI-supported learning, integrated into existing Learning Management Systems. The platform runs on container infrastructure and uses current OpenAI models, with security and auditability designed in.
Real-time speech AI for language learning
We built an AI-driven platform for real-time speech-to-speech conversations between students and virtual language buddies. Teachers create CEFR-aligned scenarios with gamified challenges, using Azure OpenAI realtime models for authentic language practice.
The same engineers stay on the work.
- Documented decisions. Architecture, trade-offs, and runbooks live in the same repository as the code: reviewable on day one and on day 200.
- Monthly WTF sessions. Engineers walk through customer work, technical deep-dives, experiments, and what didn’t work. What The Faqtory!: our standing internal review.
- Standing learning investment. Active Pluralsight and A Cloud Guru access, certification time, and study agreements where the depth justifies it.
The point isn’t novelty. The point is that two years in, the platform still makes sense to the people running it. Read about our engineering culture